Fusion of Remote Sensing Images Using Improved ICA Mergers Based on Wavelet Decomposition

Abstract

Spectral distortion is one of the most significant problems in the field of remote sensing image fusion. In former studies, we found the fusion method based on independent component analysis (ICA) could solve this problem effectively, and attain a better balance between spectral and spatial information of fused image. However, this method may lead to spectral distort in a few local regions unavoidably. In this paper, an improved ICA fusion method is proposed. Improvement mainly includes two aspects. Firstly, a convenient way which uses negentropy to measure the nongaussianity of IC is presented to select main body independent component (MBIC); secondly, in order to avoid too much spatial information caused by replacing MBIC with panchromatic (PAN) image directly, a wavelet decomposition is applied to extract the detail information of PAN image. The results show that the proposed method can have a better trade-off between spectral and spatial information. Moreover, compared with ICA fusion method, it can not only improve the spatial resolution of fused image, but also eliminate the drawback of spectral distortion of ICA fusion method in some local regions.